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Introduction: This analysis is based on the outputs of pairwise comparison of differential gene expression generated by this template. It uses results from 2 pairwise comparisons of 2 sample groups vs. their corresponding control groups and compares how these 2 sample groups are different from each other in terms of their sample-control differences (delta-delta). An example of such analysis is the different responses of two cell types to the treatment of the same drug.

Liver_Female vs. Liver_Male
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Project

Title

Treatment of pregnant mice with LPS

Description

The project studies effect of LPS on 3 mouse tissues; uterus of mother mice, placenta of newborns and liver of newborns. Additionally, newborns were separated by genders.

Analysis

This analysis is interested in the gender difference in terms of response to LPS in infant mouse livers.

Pairwise comparisons

This report compares the results of the 2 following pairwise comparisons. Click links to view full results of individual comparisons:

Gene-level comparison

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Global delta-delta correlation

Both comparisons reported the log ratio of 2 group means for each gene. The global agreement of log ratios of all genes indicates how much the results of these 2 comparisons are similar to or different from each other. Full table of gene-level statistics side-by-side is here.

Figure 1 This plot shows the global correlation (correlation coefficient = 0.597) between the 2 pairwise comparisons: Liver_Female and Liver_Male. DEGs identified by both comparisons are highlighted.

Differentially expression genes (DEGs)

Both comparisons identified DEGs of 2 compared groups. Overlapped DEGs identified by both comparisons are worthy of a closer look.

Table 1 Number of DEGs identified by both comparisons:

  Liver_Female Liver_Male
Higher in Placenta_F_LPS/Placenta_M_LPS 530 484
Lower in Placenta_F_LPS/Placenta_M_LPS 559 351

Figure 2 Overlapping of DEGs. All combinations of differential expression towards opposite directions were plotted and Fisher’s exact test was performed to evaluate the significance of overlapping or lack of overlapping.

Click links to view tables of overlapping DEGs:

ANOVA

2-way ANOVA analysis was performed to identify genes responding to Treatment differently in different Gender. The analysis reported 3 p values, corresponding to the effect of Treatment, Gender, and their interaction. The analysis identified 115 significant genes with interaction p values less than 0.01. The ANOVA results were summarized in a table here.

Figure 3 This is the top gene having the most significant interactive p value (4.095125510^{-5}).

Gene set-level comparison

Genes are often grouped into pre-defined gene sets according to their function, interaction, location, etc. Analysis then can be performed on genes in the same gene set as a unit instead of individual genes.

Gene set average

Average differential expression of genes in the same gene set. The gene set-level statistics were fully summarized in this table here.

Figure 4 Each dot represents a gene set and the average log-ratio of all genes in this gene set. The averages were calculated with the log-ratio value of genes (left panel) and the absolute of the log-ratios (right panel). The correlation coefficients are 0.951 and 0.9767 respectively.

Gene set over-representation analysis (ORA)

Each 2-group comparison performs gene set over-representation analysis (ORA) that identifies gene sets over-represented with differentially expressed genes. The results of ORA of both 2-group comparisons are summarized and compared here. The ORA of each gene set reports an odds ratio and p value. These statistics from both comparisons were combined and listed side-by-side, as well as the difference of their odds ratios and ratio of their p values (p set to 0.5 when not available), in this table here

Table 2 Gene sets were broken down into subgroups by their sources. Click on the numbers of over-represented gene sets to see a full list.

Higher_in_Placenta_F_LPS Lower_in_Placenta_F_LPS Higher_in_Placenta_M_LPS Lower_in_Placenta_M_LPS
BioSystems 1944 368 1781 401
KEGG 123 67 107 16
MSigDb 2699 782 2412 815
OMIM 1 0 1 0
PubTator 1775 33 1696 48

Figure 5 The overlapping of over-represented gene sets from both comparisons.

Click links to view tables of overlapping significant gene sets from ORA:

Gene set enrichment analysis (GSEA)

Each 2-group comparison performs gene set enrichment analysis (GSEA) on genes ranked by their differential expression. The results of GSEA of both 2-group comparisons are summarized and compared here. The GSEA of each gene set reports an enrichment score and p value. These statistics from both comparisons were combined and listed side-by-side in this table here

Table 3 Gene sets were broken down into subgroups by collections. Click on the numbers of enriched gene sets to see a full list.

Higher_in_Placenta_F_LPS Lower_in_Placenta_F_LPS Higher_in_Placenta_M_LPS Lower_in_Placenta_M_LPS
BioCarta_pathway 84 1 69 5
GO_BP 2001 265 1614 336
GO_CC 190 107 118 102
GO_MF 273 151 187 154
KEGG_compound 24 24 40 6
KEGG_enzyme 3 3 2 1
KEGG_module 9 12 6 13
KEGG_pathway 121 19 102 18
KEGG_reaction 8 9 17 7
MsigDb_Chemical_and_genetic_perturbations 1496 123 1160 177
MSigDb_Hallmark 33 3 27 3
MSigDb_Immunologic_signatures 1015 328 869 363
MSigDb_MicroRNA_targets 33 14 5 69
MSigDb_Oncogenic_signatures 115 6 108 9
MSigDb_Positional 26 31 18 24
MSigDb_TF_targets 160 88 83 161
OMIM_gene 17 4 14 0
REACTOME_pathway 341 116 287 81
Wiki_pathway 70 9 57 1

Figure 6 Nominal enrichment scores from both comparisons. Each dot represents a gene set. Gene sets with p values less than 0.01 from both comparisons are highlighted.

Figure 7 The overlapping of over-represented gene sets from both comparisons.

Click links to view tables of overlapping significant gene sets from GSEA:

Gene clustering

Genes with significant ANOVA p values (p <= ‘r yml\(input\)geneset\(cluster\)panova’) were used as seeds to perform a gene-gene clustering analysis and 4 clusters were identified. ORA was performed on the clusters to identify their functional association (see table below);

Table 4 This table lists the number of genes in each cluster (click the numbers to see gene lists), the average expression of all genes in a cluster of all sample groups, and then the gene sets over-represented in each cluster (click the numbers to see gene set lists). The gene expression levels were normalized so the mean of the control groups equals to 0 and the mean of the treatment groups is the number of standard deviations.

ID Size Placenta_F_Ctrl Placenta_F_LPS Placenta_M_Ctrl Placenta_M_LPS Gene_set
Cluster_1 148 0 1.8080 0 0.6408 2006
Cluster_2 101 0 0.5263 0 -1.3945 334
Cluster_3 99 0 0.2910 0 1.7535 371
Cluster_4 97 0 -1.4890 0 0.4630 110

Figure 8 This plot shows below the average expression levels of each cluster. Data was normalized before the analysis, so the mean of the control groups was zero and the standard deviation of all samples of each gene was 1.0. Values indicate number of standard deviation from mean of relative control group.

Figure 9 This plot summarizes the group means and standard errors of all clusters.

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